Microsoft Researchers Developed An AI That Got A Perfect Score On 'Ms. Pac-Man'

Researchers at Maluuba, a deep learning startup that was recently acquired by Microsoft, have developed an artificial intelligence system that became the first player - human or computer - to achieve a perfect 999,990 score in Ms. Pac-Man.

Using deep learning to develop programs that can defeat video games isn't a new feat, but this accomplishment is notable for several reasons.

First of all, it's notable because of the type of game chosen. The old 1980s arcade games weren't designed to be beaten - they were designed to keep people pumping in quarters. And when Ms. Pac-Man was developed, it was actually programmed to be less predictable than the original Pac-Man, so that it would be tougher for players to beat it.

The second and perhaps most notable aspect of this accomplishment, though, is the approach that the researchers took to solve Ms. Pac-Man. Rather than develop a single intelligent agent to learn the game, as other researchers have done, this team instead used a number of simpler intelligent agents to learn a single aspect of the game. For example, there are agents learning about ghost behavior, about fruit behavior, about pellet behavior, etc.

Each individual agent (there's over 100), develops a course of action it thinks Ms. Pac-Man should follow based on the small part of the game it's focused on. Those decisions are then aggregated, and the program moves Ms. Pac-Man based on the weighted average of preferences from the individual agents.

"By breaking up a problem in this way, it becomes easier to learn," explains one of the researchers in a video. "As there are now many agents that learn very simple tasks, instead of just one agent that learns a very complex task."

The researchers believe that breaking up complex problems into simpler, smaller problems can make it much easier for deep learning systems to be able to handle more complex behavior. That, in turn, could be applied to lots of real-world tasks that AI could be applied to in the future. Even if a problem can only be broken into two or three parts, the researchers say, that could result in an "exponential decrease of the problem size."

You can check out a video explaining how the system works below:

The researchers have also written a paper documenting their AI system, which you can read here.